NEURON {
POINT_PROCESS pyr2pyr
NONSPECIFIC_CURRENT i_nmda, i_ampa
RANGE initW
RANGE Cdur_nmda, AlphaTmax_nmda, Beta_nmda, Erev_nmda, gbar_nmda, W_nmda, on_nmda, g_nmda
RANGE Cdur_ampa, AlphaTmax_ampa, Beta_ampa, Erev_ampa, gbar_ampa, W_ampa, on_ampa, g_ampa
RANGE ECa, ICa, P0, fCa, tauCa, iCatotal
RANGE Cainf, pooldiam, z
RANGE lambda1, lambda2, threshold1, threshold2
RANGE fmax, fmin, Wmax, Wmin, maxChange, normW, scaleW
RANGE pregid,postgid
:Added by Ali
RANGE F, f, tauF, D1, d1, tauD1, D2, d2, tauD2
RANGE facfactor
RANGE aACH, bACH, aDA, bDA, wACH, wDA, calcium
}
UNITS {
(mV) = (millivolt)
(nA) = (nanoamp)
(uS) = (microsiemens)
FARADAY = 96485 (coul)
pi = 3.141592 (1)
}
PARAMETER {
: parameters are vars assigned by user or changed by hoc. THey appear in nrnpointmenu
initW = 5
Cdur_nmda = 10 (ms)
AlphaTmax_nmda = .088 (/ms)
Beta_nmda = 0.0033 (/ms) :.008
Erev_nmda = 0 (mV)
gbar_nmda = 0.0017 (uS) :.6e-3
Cdur_ampa = 2.4(ms) :5.31
AlphaTmax_ampa = 0.58 (/ms) :0.117
Beta_ampa = 0.091 (/ms)
Erev_ampa = 0 (mV)
gbar_ampa = 1.7e-3 (uS)
ECa = 120
Cainf = 50e-6 (mM)
pooldiam = 1.8172 (micrometer)
z = 2
tauCa = 50 (ms)
P0 = .015
fCa = .024
lambda1 = 2.5
lambda2 = .01
threshold1 = 0.2 (uM)
threshold2 = 0.4 (uM)
fmax = 3
fmin = .8
:Added by Ali
ACH = 1
DA = 1
LearningShutDown = 1
facfactor = 1
: the (1) is needed for the range limits to be effective
f = 1 (1) < 0, 1e9 > : facilitation
tauF = 1 (ms) < 1e-9, 1e9 >
d1 = 1 (1) < 0, 1 > : fast depression
tauD1 = 1 (ms) < 1e-9, 1e9 >
d2 = 1 (1) < 0, 1 > : slow depression
tauD2 = 1 (ms) < 1e-9, 1e9 >
aACH = 1
bACH = 0
wACH = 0
aDA = 1
bDA = 0
wDA = 0
}
ASSIGNED {
: These are vars calculated by Neuron hoc or by the mechanism mod itself
v (mV)
i_nmda (nA)
g_nmda (uS)
on_nmda
W_nmda
i_ampa (nA)
g_ampa (uS)
on_ampa
W_ampa
t0 (ms)
ICa (mA)
Afactor (mM/ms/nA)
iCatotal (mA)
dW_ampa
Wmax
Wmin
maxChange
normW
scaleW
pregid
postgid
:Added by Ali
calcium
tsyn
fa
F
D1
D2
}
STATE { r_nmda r_ampa Capoolcon }
INITIAL {
on_nmda = 0
r_nmda = 0
W_nmda = initW
on_ampa = 0
r_ampa = 0
W_ampa = initW
t0 = -1
:Wmax = 2*initW
:Wmin = 0.25*initW
maxChange = (Wmax-Wmin)/10
dW_ampa = 0
Capoolcon = Cainf
Afactor = 1/(z*FARADAY*4/3*pi*(pooldiam/2)^3)*(1e6)
:Added by Ali
tsyn = -1e30
fa =0
F = 1
D1 = 1
D2 = 1
}
BREAKPOINT {
SOLVE release METHOD cnexp
}
DERIVATIVE release {
if (t0>0) {
if (t-t0 < Cdur_nmda) {
on_nmda = 1
} else {
on_nmda = 0
}
if (t-t0 < Cdur_ampa) {
on_ampa = 1
} else {
on_ampa = 0
}
}
r_nmda' = AlphaTmax_nmda*on_nmda*(1-r_nmda) -Beta_nmda*r_nmda
r_ampa' = AlphaTmax_ampa*on_ampa*(1-r_ampa) -Beta_ampa*r_ampa
dW_ampa = eta(Capoolcon)*(lambda1*omega(Capoolcon, threshold1, threshold2)-lambda2*W_ampa)*dt
: Limit for extreme large weight changes
if (fabs(dW_ampa) > maxChange) {
if (dW_ampa < 0) {
dW_ampa = -1*maxChange
} else {
dW_ampa = maxChange
}
}
:Normalize the weight change
normW = (W_ampa-Wmin)/(Wmax-Wmin)
if (dW_ampa < 0) {
scaleW = sqrt(fabs(normW))
} else {
scaleW = sqrt(fabs(1.0-normW))
}
W_ampa = W_ampa + dW_ampa*scaleW *(1+ (wACH * (ACH - 1))) * LearningShutDown
:Weight value limits
if (W_ampa > Wmax) {
W_ampa = Wmax
} else if (W_ampa < Wmin) {
W_ampa = Wmin
}
g_nmda = gbar_nmda*r_nmda * facfactor
i_nmda = W_nmda*g_nmda*(v - Erev_nmda)*sfunc(v)
g_ampa = gbar_ampa*r_ampa * facfactor
i_ampa = W_ampa*g_ampa*(v - Erev_ampa) * (1 + (bACH * (ACH-1)))*(aDA + (bDA * (DA-1)))
ICa = P0*g_nmda*(v - ECa)*sfunc(v)
Capoolcon'= -fCa*Afactor*ICa + (Cainf-Capoolcon)/tauCa
}
NET_RECEIVE(dummy_weight) {
t0 = t :spike time for conductance opening
:Added by Ali, Synaptic facilitation
F = 1 + (F-1)* exp(-(t - tsyn)/tauF)
D1 = 1 - (1-D1)*exp(-(t - tsyn)/tauD1)
D2 = 1 - (1-D2)*exp(-(t - tsyn)/tauD2)
:printf("%g\t%g\t%g\t%g\t%g\t%g\n", t, t-tsyn, F, D1, D2, facfactor)
tsyn = t
facfactor = F * D1 * D2
F = F * f
if (F > 30) {
F=30
}
D1 = D1 * d1
D2 = D2 * d2
:printf("\t%g\t%g\t%g\n", F, D1, D2)
}
:::::::::::: FUNCTIONs and PROCEDUREs ::::::::::::
FUNCTION sfunc (v (mV)) {
UNITSOFF
sfunc = 1/(1+0.33*exp(-0.06*v))
UNITSON
}
FUNCTION eta(Cani (mM)) {
LOCAL taulearn, P1, P2, P4, Cacon
P1 = 0.1
P2 = P1*1e-4
P4 = 1
Cacon = Cani*1e3
taulearn = P1/(P2+Cacon*Cacon*Cacon)+P4
eta = 1/taulearn*0.001
}
FUNCTION omega(Cani (mM), threshold1 (uM), threshold2 (uM)) {
LOCAL r, mid, Cacon
Cacon = Cani*1e3
r = (threshold2-threshold1)/2
mid = (threshold1+threshold2)/2
if (Cacon <= threshold1) { omega = 0}
else if (Cacon >= threshold2) { omega = 1/(1+50*exp(-50*(Cacon-threshold2)))}
else {omega = -sqrt(r*r-(Cacon-mid)*(Cacon-mid))}
}